An Econometric Model of Serial Correlation and Illiquidity in Hedge Fund Returns

92 Pages Posted: 7 Mar 2003  

Mila Getmansky

University of Massachusetts at Amherst - Eugene M. Isenberg School of Management - Department of Finance

Andrew W. Lo

Massachusetts Institute of Technology (MIT) - Sloan School of Management; National Bureau of Economic Research (NBER); Massachusetts Institute of Technology (MIT) - Computer Science and Artificial Intelligence Laboratory (CSAIL)

Igor Makarov

London Business School

Multiple version iconThere are 2 versions of this paper

Date Written: March 1, 2003

Abstract

The returns to hedge funds and other alternative investments are often highly serially correlated in sharp contrast to the returns of more traditional investment vehicles such as long-only equity portfolios and mutual funds. In this paper, we explore several sources of such serial correlation and show that the most likely explanation is illiquidity exposure, i.e., investments in securities that are not actively traded and for which market prices are not always readily available. For portfolios of illiquid securities, reported returns will tend to be smoother than true economic returns, which will understate volatility and increase risk-adjusted performance measures such as the Sharpe ratio. We propose an econometric model of illiquidity exposure and develop estimators for the smoothing profile as well as a smoothing-adjusted Sharpe ratio. For a sample of 908 hedge funds drawn from the TASS database, we show that our estimated smoothing coefficients vary considerably across hedge-fund style categories and may be a useful proxy for quantifying illiquidity exposure.

Keywords: Hedge Funds, Serial Correlation, Market Efficiency, Performance Smoothing, Liquidity

JEL Classification: G12

Suggested Citation

Getmansky, Mila and Lo, Andrew W. and Makarov, Igor, An Econometric Model of Serial Correlation and Illiquidity in Hedge Fund Returns (March 1, 2003). MIT Sloan Working Paper No. 4288-03; MIT Laboratory for Financial Engineering Working Paper No. LFE-1041A-03; EFMA 2003 Helsinki Meetings. Available at SSRN: https://ssrn.com/abstract=384700 or http://dx.doi.org/10.2139/ssrn.384700

Mila Getmansky Sherman

University of Massachusetts at Amherst - Eugene M. Isenberg School of Management - Department of Finance ( email )

Amherst, MA 01003-4910
United States

Andrew W. Lo (Contact Author)

Massachusetts Institute of Technology (MIT) - Sloan School of Management ( email )

100 Main Street
E62-618
Cambridge, MA 02142
United States
617-253-0920 (Phone)
781 891-9783 (Fax)

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National Bureau of Economic Research (NBER) ( email )

1050 Massachusetts Avenue
Cambridge, MA 02138
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Massachusetts Institute of Technology (MIT) - Computer Science and Artificial Intelligence Laboratory (CSAIL)

Stata Center
Cambridge, MA 02142
United States

Igor Makarov

London Business School ( email )

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Regent's Park
London, London NW1 4SA
United Kingdom
+44 (0)20 7000 8265 (Phone)
+44 (0)20 7000 8201 (Fax)

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